Economics and Abstraction

David Brooks has a great column in the New York Times arguing that technocratic management of the economy leaves something to be desired. He particularly focuses on the growing disillusion with attempts to manage our response to the economic crisis. My favorite part is this:

The liberal technicians have an impressive certainty about them. They have amputated those things that can’t be contained in models, like emotional contagions, cultural particularities and webs of relationships. As a result, everything is explainable and predictable. They can stand on the platform of science and dismiss the poor souls down below.

Yet over the past 21 months, it has been harder to groove to their certainty.

In practice, the problem of excessive abstraction by economic theory that Brooks identifies becomes increasingly severe as we try to evaluate the effects of proposed interventions and programs over years and decades, rather than months and quarters.

Consider the role of very low interest rates in stimulating economic growth in the software industry where I work. Easy monetary policy, along with various other forms of stimulus, has at least in part, likely worked as advertised; it has likely stimulated some extremely-difficult-to-quantify general economic growth, which has in turn created demand for enterprise software, among many other things. And low interest rates probably have resulted in certain additional development projects within large companies being greenlighted because they face a lower discount rate. In fact, many traditional large enterprise software companies have built large cash hoards. But they are mostly using them to finance acquisitions, not to expand capacity and increase aggregate output. Why this is so turns out to be important for understanding the potential effects of this policy on the industry.

The biggest reason is that a series of disruptive technical / business model innovations – most prominently, Software-as-a-Service (SaaS) and open-source – is transforming the industry. The rational incentive of the incumbent managers is to suppress the innovations, and at best, slow-walk them and channel them in directions consistent with their current business models. What’s happening right now is the jockeying between entrepreneurs, incumbent company management teams and the capital markets to seize the potential value that these innovations are unleashing.

Large company growth will disproportionately come not from just adding more of the current “capacity” (mostly people) in response to stimulus, but different kinds of capacity that are required for these new business models. For example, more software engineers trained in traditional languages and accustomed to working on large, structured projects are less useful for growth than engineers with experience in web-focused technologies used to working in a so-called agile development environment. And it’s not as simple as incumbent companies simply changing their hiring specs, because it is very difficult to transform settled company expertise, systems, compensation plans, culture and so forth to operate in this new environment.

Large software companies do not have plans on the drawing boards for the moral equivalent of a new ball-bearing factory if only demand were higher – their primary strategic problem is this regard is that they don’t know how to build the new capacity. But the existence of the competitive threat forces their hand, and they buy the new kind of capacity in the form of corporate acquisitions.

One major effect of a Fed policy of easy money, then, is that large software companies can go borrow lots of money cheaply, and then use this acquire entrepreneurial companies that usually require more equity financing rather than debt financing. This does not add capacity to world, but simply transfers management control over some very important assets from entrepreneurs to incumbents.

Will this lead to higher or lower economic output in 2015, 2020 and 2030? I don’t know. But then again, neither does anybody else.

The example I have chosen to highlight focuses on the complications in trying to forecast the impact of lower interest rates in the software industry created by the emergence of new technologies and business models. But of course, there are many other complicated effects of very low interest rates on the software industry beyond simply pumping up aggregate demand – impacting everything from the feasibility of leveraged buy-outs to the re-opening of the IPO window. Each will advantage or disadvantage some part so the industry at the expense of others. And stimulus can be anything from low interest rates to running deficits to quantitative easing. And the software industry in one small part of the overall economy. So this is an example of one complication for one type of stimulus in one industry.

Where is any of this complexity captured in econometric models that purport to explain how fiscal deficits, interest rates and quantitative easing are driving everything from car dealerships to television broadcasters to consumers of dog food, all of whom face their own unique dynamics? But without it, I doubt the ability of any model to forecast the long-run impacts of a multi-trillion dollar program to intervene in the economy in the name of creating self-sustaining growth in the long -term. All I can say with confidence is that if you believe as I do that a good rough rule-of-thumb is that “over any sustained period markets supported b y an appropriate culture will do a better job than politicians in allocating resources to generate high economic growth,” then at some point, the distortions created by such a policy would likely outweigh any benefits it can create.

In an emergency, the idea of stimulus is not an inherently bad one; in fact, I have advocated it in certain circumstances. But it is inherently dangerous. Its effects are at best only extremely loosely predictable in the short-run; it is addictive; and it is likely pernicious if sustained.

From at least the time of J.S. Mill, the fundamental methodology of economics has been to use introspection to develop theories about human behavior, systematize them into theories, and then try to compare the predictions of these theories to the real world. For reasons I have gone into at boring length, it is very difficult to conduct such tests of useful, non-obvious rules that predict the effects of our proposed interventions reliably in economics and other social sciences. The big problem with most economic theories that claim to be able to guide our interventions with confidence is not usually that the causal pathway that they propose is incorrect, but that it’s radically incomplete. It is typically one of an all-but-innumerable array of causes that are interconnected in a maze of causation that produces highly unpredictable outcomes as a result of any intervention. Despite confident assertions by academicians, the Law of Unintended Consequences remains in force.